Overview

Dataset statistics

Number of variables40
Number of observations297064
Missing cells1798160
Missing cells (%)15.1%
Total size in memory57.0 MiB
Average record size in memory201.1 B

Variable types

Categorical18
Numeric22

Alerts

admitdiagnosis has a high cardinality: 425 distinct values High cardinality
eyes is highly correlated with motor and 1 other fieldsHigh correlation
motor is highly correlated with eyes and 1 other fieldsHigh correlation
verbal is highly correlated with eyes and 1 other fieldsHigh correlation
creatinine is highly correlated with bunHigh correlation
bun is highly correlated with creatinineHigh correlation
eyes has 4580 (1.5%) missing values Missing
motor has 4580 (1.5%) missing values Missing
verbal has 4580 (1.5%) missing values Missing
urine has 144170 (48.5%) missing values Missing
wbc has 70128 (23.6%) missing values Missing
temperature has 12256 (4.1%) missing values Missing
sodium has 57374 (19.3%) missing values Missing
ph has 227190 (76.5%) missing values Missing
hematocrit has 63870 (21.5%) missing values Missing
creatinine has 58328 (19.6%) missing values Missing
albumin has 179366 (60.4%) missing values Missing
pao2 has 227190 (76.5%) missing values Missing
pco2 has 227190 (76.5%) missing values Missing
bun has 59454 (20.0%) missing values Missing
glucose has 33346 (11.2%) missing values Missing
bilirubin has 190228 (64.0%) missing values Missing
fio2 has 227190 (76.5%) missing values Missing
urine is highly skewed (γ1 = 41.44491087) Skewed
meds has 292484 (98.5%) zeros Zeros
urine has 3348 (1.1%) zeros Zeros
age has 10446 (3.5%) zeros Zeros

Reproduction

Analysis started2022-03-28 15:26:19.434688
Analysis finished2022-03-28 15:27:38.257124
Duration1 minute and 18.82 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

intubated
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size290.3 KiB
0
251532 
1
45532 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0251532
84.7%
145532
 
15.3%

Length

2022-03-29T00:27:38.319489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-29T00:27:38.385032image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0251532
84.7%
145532
 
15.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

vent
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size290.3 KiB
0
224046 
1
73018 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0224046
75.4%
173018
 
24.6%

Length

2022-03-29T00:27:38.439657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-29T00:27:38.501736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0224046
75.4%
173018
 
24.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

dialysis
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size290.3 KiB
0
286084 
1
 
10980

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0286084
96.3%
110980
 
3.7%

Length

2022-03-29T00:27:38.567947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-29T00:27:38.624562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0286084
96.3%
110980
 
3.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

eyes
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing4580
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean3.48994133
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2022-03-29T00:27:38.671414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q34
95-th percentile4
Maximum4
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9326044648
Coefficient of variation (CV)0.267226402
Kurtosis1.801641582
Mean3.48994133
Median Absolute Deviation (MAD)0
Skewness-1.757480524
Sum1020752
Variance0.8697510877
MonotonicityNot monotonic
2022-03-29T00:27:38.744978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
4208624
70.2%
343860
 
14.8%
125324
 
8.5%
214676
 
4.9%
(Missing)4580
 
1.5%
ValueCountFrequency (%)
125324
 
8.5%
214676
 
4.9%
343860
 
14.8%
4208624
70.2%
ValueCountFrequency (%)
4208624
70.2%
343860
 
14.8%
214676
 
4.9%
125324
 
8.5%

motor
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct6
Distinct (%)< 0.1%
Missing4580
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean5.48645396
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2022-03-29T00:27:38.825022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median6
Q36
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.261227692
Coefficient of variation (CV)0.2298803017
Kurtosis6.663117312
Mean5.48645396
Median Absolute Deviation (MAD)0
Skewness-2.758560644
Sum1604700
Variance1.59069529
MonotonicityNot monotonic
2022-03-29T00:27:38.902830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
6232126
78.1%
525122
 
8.5%
116838
 
5.7%
415424
 
5.2%
31852
 
0.6%
21122
 
0.4%
(Missing)4580
 
1.5%
ValueCountFrequency (%)
116838
 
5.7%
21122
 
0.4%
31852
 
0.6%
415424
 
5.2%
525122
 
8.5%
6232126
78.1%
ValueCountFrequency (%)
6232126
78.1%
525122
 
8.5%
415424
 
5.2%
31852
 
0.6%
21122
 
0.4%
116838
 
5.7%

verbal
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)< 0.1%
Missing4580
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean4.020903708
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2022-03-29T00:27:38.979823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median5
Q35
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.538011156
Coefficient of variation (CV)0.382503852
Kurtosis-0.1660657703
Mean4.020903708
Median Absolute Deviation (MAD)0
Skewness-1.249111447
Sum1176050
Variance2.365478317
MonotonicityNot monotonic
2022-03-29T00:27:39.058671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
5185940
62.6%
152202
 
17.6%
437628
 
12.7%
310208
 
3.4%
26506
 
2.2%
(Missing)4580
 
1.5%
ValueCountFrequency (%)
152202
 
17.6%
26506
 
2.2%
310208
 
3.4%
437628
 
12.7%
5185940
62.6%
ValueCountFrequency (%)
5185940
62.6%
437628
 
12.7%
310208
 
3.4%
26506
 
2.2%
152202
 
17.6%

meds
Real number (ℝ≥0)

ZEROS

Distinct2
Distinct (%)< 0.1%
Missing1540
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean0.01028681258
Minimum0
Maximum1
Zeros292484
Zeros (%)98.5%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2022-03-29T00:27:39.144258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1009010828
Coefficient of variation (CV)9.808780132
Kurtosis92.22381648
Mean0.01028681258
Median Absolute Deviation (MAD)0
Skewness9.706863156
Sum3040
Variance0.01018102851
MonotonicityNot monotonic
2022-03-29T00:27:39.219256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0292484
98.5%
13040
 
1.0%
(Missing)1540
 
0.5%
ValueCountFrequency (%)
0292484
98.5%
13040
 
1.0%
ValueCountFrequency (%)
13040
 
1.0%
0292484
98.5%

urine
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct35577
Distinct (%)23.3%
Missing144170
Missing (%)48.5%
Infinite0
Infinite (%)0.0%
Mean1806.111998
Minimum0
Maximum269323.7472
Zeros3348
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-03-29T00:27:39.320247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile105.4944
Q1783.648
median1443.5712
Q32404.944
95-th percentile4585.9392
Maximum269323.7472
Range269323.7472
Interquartile range (IQR)1621.296

Descriptive statistics

Standard deviation1858.122551
Coefficient of variation (CV)1.02879697
Kurtosis5651.161437
Mean1806.111998
Median Absolute Deviation (MAD)767.664
Skewness41.44491087
Sum276143687.8
Variance3452619.415
MonotonicityNot monotonic
2022-03-29T00:27:39.439870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03348
 
1.1%
1799.9712100
 
< 0.1%
1439.942472
 
< 0.1%
2399.932872
 
< 0.1%
899.942472
 
< 0.1%
1199.923260
 
< 0.1%
2117.577658
 
< 0.1%
2057.097658
 
< 0.1%
1674.345658
 
< 0.1%
1090.886458
 
< 0.1%
Other values (35567)148938
50.1%
(Missing)144170
48.5%
ValueCountFrequency (%)
03348
1.1%
0.691210
 
< 0.1%
0.777634
 
< 0.1%
0.86426
 
< 0.1%
0.950418
 
< 0.1%
1.036826
 
< 0.1%
1.123224
 
< 0.1%
1.209624
 
< 0.1%
1.29622
 
< 0.1%
1.382424
 
< 0.1%
ValueCountFrequency (%)
269323.74722
< 0.1%
65063.60642
< 0.1%
39242.79362
< 0.1%
35636.88962
< 0.1%
33966.25922
< 0.1%
32180.5442
< 0.1%
30030.99842
< 0.1%
28544.91842
< 0.1%
27576.89282
< 0.1%
26954.72642
< 0.1%

wbc
Real number (ℝ≥0)

MISSING

Distinct3676
Distinct (%)1.6%
Missing70128
Missing (%)23.6%
Infinite0
Infinite (%)0.0%
Mean12.29150853
Minimum0.01
Maximum198.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-03-29T00:27:39.562250image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile4.26
Q17.4
median10.4
Q315.2
95-th percentile25.3
Maximum198.1
Range198.09
Interquartile range (IQR)7.8

Descriptive statistics

Standard deviation8.186186495
Coefficient of variation (CV)0.6660034018
Kurtosis62.26126752
Mean12.29150853
Median Absolute Deviation (MAD)3.6
Skewness4.912659491
Sum2789385.78
Variance67.01364933
MonotonicityNot monotonic
2022-03-29T00:27:39.687459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.41800
 
0.6%
8.41780
 
0.6%
7.61764
 
0.6%
8.21750
 
0.6%
81748
 
0.6%
8.81742
 
0.6%
8.61728
 
0.6%
7.81726
 
0.6%
71714
 
0.6%
7.91708
 
0.6%
Other values (3666)209476
70.5%
(Missing)70128
 
23.6%
ValueCountFrequency (%)
0.012
 
< 0.1%
0.024
 
< 0.1%
0.032
 
< 0.1%
0.042
 
< 0.1%
0.072
 
< 0.1%
0.082
 
< 0.1%
0.092
 
< 0.1%
0.1166
0.1%
0.118
 
< 0.1%
0.138
 
< 0.1%
ValueCountFrequency (%)
198.12
< 0.1%
189.72
< 0.1%
188.22
< 0.1%
186.22
< 0.1%
184.52
< 0.1%
183.62
< 0.1%
182.92
< 0.1%
179.32
< 0.1%
178.82
< 0.1%
177.82
< 0.1%

temperature
Real number (ℝ≥0)

MISSING

Distinct368
Distinct (%)0.1%
Missing12256
Missing (%)4.1%
Infinite0
Infinite (%)0.0%
Mean36.42329338
Minimum20
Maximum42.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-03-29T00:27:39.811940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile35.3
Q136.2
median36.5
Q336.7
95-th percentile37.3
Maximum42.3
Range22.3
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.9318595877
Coefficient of variation (CV)0.0255841661
Kurtosis35.79249153
Mean36.42329338
Median Absolute Deviation (MAD)0.3
Skewness-2.632689506
Sum10373645.34
Variance0.8683622912
MonotonicityNot monotonic
2022-03-29T00:27:39.922223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.430382
 
10.2%
36.627826
 
9.4%
36.726166
 
8.8%
36.321332
 
7.2%
36.520172
 
6.8%
36.818784
 
6.3%
36.214970
 
5.0%
36.113948
 
4.7%
36.912692
 
4.3%
369818
 
3.3%
Other values (358)88718
29.9%
(Missing)12256
 
4.1%
ValueCountFrequency (%)
208
< 0.1%
20.22
 
< 0.1%
20.34
< 0.1%
20.44
< 0.1%
20.62
 
< 0.1%
20.86
< 0.1%
212
 
< 0.1%
21.16
< 0.1%
21.24
< 0.1%
21.42
 
< 0.1%
ValueCountFrequency (%)
42.32
 
< 0.1%
42.12
 
< 0.1%
422
 
< 0.1%
41.94
 
< 0.1%
41.832
 
< 0.1%
41.86
< 0.1%
41.76
< 0.1%
41.66
< 0.1%
41.510
< 0.1%
41.412
< 0.1%

respiratoryrate
Real number (ℝ≥0)

Distinct80
Distinct (%)< 0.1%
Missing1920
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean25.58925745
Minimum4
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-03-29T00:27:40.037059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5
Q111
median28
Q336
95-th percentile53
Maximum60
Range56
Interquartile range (IQR)25

Descriptive statistics

Standard deviation15.12073009
Coefficient of variation (CV)0.5909014795
Kurtosis-0.9258238071
Mean25.58925745
Median Absolute Deviation (MAD)14
Skewness0.2727084181
Sum7552515.8
Variance228.6364783
MonotonicityNot monotonic
2022-03-29T00:27:40.150769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1014416
 
4.9%
1213838
 
4.7%
1113042
 
4.4%
412052
 
4.1%
911646
 
3.9%
3010300
 
3.5%
810252
 
3.5%
2810168
 
3.4%
299572
 
3.2%
319050
 
3.0%
Other values (70)180808
60.9%
ValueCountFrequency (%)
412052
4.1%
56852
2.3%
5.94
 
< 0.1%
66884
2.3%
6.22
 
< 0.1%
6.82
 
< 0.1%
6.92
 
< 0.1%
77948
2.7%
7.14
 
< 0.1%
7.22
 
< 0.1%
ValueCountFrequency (%)
603082
1.0%
592088
0.7%
581644
0.6%
571576
0.5%
561666
0.6%
551634
0.6%
541670
0.6%
531596
0.5%
521834
0.6%
511760
0.6%

sodium
Real number (ℝ≥0)

MISSING

Distinct178
Distinct (%)0.1%
Missing57374
Missing (%)19.3%
Infinite0
Infinite (%)0.0%
Mean137.9672777
Minimum91
Maximum195
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-03-29T00:27:40.271126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum91
5-th percentile129
Q1135
median138
Q3141
95-th percentile146
Maximum195
Range104
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.569033335
Coefficient of variation (CV)0.04036488526
Kurtosis5.690721177
Mean137.9672777
Median Absolute Deviation (MAD)3
Skewness0.02455510042
Sum33069376.8
Variance31.01413228
MonotonicityNot monotonic
2022-03-29T00:27:40.387263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13824036
8.1%
13923570
 
7.9%
14022040
 
7.4%
13721906
 
7.4%
13619254
 
6.5%
14117892
 
6.0%
13515230
 
5.1%
14213934
 
4.7%
13412478
 
4.2%
1439384
 
3.2%
Other values (168)59966
20.2%
(Missing)57374
19.3%
ValueCountFrequency (%)
912
 
< 0.1%
982
 
< 0.1%
992
 
< 0.1%
1008
 
< 0.1%
1016
 
< 0.1%
10212
 
< 0.1%
10316
< 0.1%
10416
< 0.1%
10512
 
< 0.1%
10630
< 0.1%
ValueCountFrequency (%)
1952
 
< 0.1%
1942
 
< 0.1%
1862
 
< 0.1%
1844
 
< 0.1%
1816
 
< 0.1%
1804
 
< 0.1%
17914
< 0.1%
1784
 
< 0.1%
17716
< 0.1%
17610
< 0.1%

heartrate
Real number (ℝ≥0)

Distinct201
Distinct (%)0.1%
Missing720
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean100.5133763
Minimum20
Maximum220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2022-03-29T00:27:40.503134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile48
Q187
median104
Q3120
95-th percentile147
Maximum220
Range200
Interquartile range (IQR)33

Descriptive statistics

Standard deviation30.99804699
Coefficient of variation (CV)0.3083972315
Kurtosis-0.2179472113
Mean100.5133763
Median Absolute Deviation (MAD)16
Skewness-0.2227685146
Sum29786536
Variance960.8789173
MonotonicityNot monotonic
2022-03-29T00:27:40.622705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1005856
 
2.0%
1025778
 
1.9%
1085702
 
1.9%
1045576
 
1.9%
1065524
 
1.9%
1105336
 
1.8%
985314
 
1.8%
1125274
 
1.8%
605164
 
1.7%
965150
 
1.7%
Other values (191)241670
81.4%
ValueCountFrequency (%)
20134
< 0.1%
2186
< 0.1%
22130
< 0.1%
2376
< 0.1%
24108
< 0.1%
25168
0.1%
26160
0.1%
27146
< 0.1%
28156
0.1%
29170
0.1%
ValueCountFrequency (%)
22020
< 0.1%
21910
 
< 0.1%
21826
< 0.1%
21722
< 0.1%
21612
< 0.1%
21514
< 0.1%
21414
< 0.1%
21314
< 0.1%
21210
 
< 0.1%
21110
 
< 0.1%

meanbp
Real number (ℝ≥0)

Distinct171
Distinct (%)0.1%
Missing962
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean86.88735139
Minimum40
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-03-29T00:27:40.968666image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile42
Q153
median66
Q3124
95-th percentile164
Maximum200
Range160
Interquartile range (IQR)71

Descriptive statistics

Standard deviation41.88543042
Coefficient of variation (CV)0.4820659136
Kurtosis-0.7238050254
Mean86.88735139
Median Absolute Deviation (MAD)20
Skewness0.7474130221
Sum25727518.52
Variance1754.389282
MonotonicityNot monotonic
2022-03-29T00:27:41.086384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
566912
 
2.3%
546728
 
2.3%
406580
 
2.2%
556516
 
2.2%
586514
 
2.2%
606358
 
2.1%
536348
 
2.1%
576346
 
2.1%
526324
 
2.1%
596080
 
2.0%
Other values (161)231396
77.9%
ValueCountFrequency (%)
406580
2.2%
415066
1.7%
424772
1.6%
42.662
 
< 0.1%
434422
1.5%
444604
1.5%
454588
1.5%
464862
1.6%
475100
1.7%
47.662
 
< 0.1%
ValueCountFrequency (%)
200452
0.2%
199392
0.1%
198342
0.1%
197358
0.1%
196348
0.1%
195366
0.1%
194364
0.1%
193346
0.1%
192346
0.1%
191320
0.1%

ph
Real number (ℝ≥0)

MISSING

Distinct711
Distinct (%)1.0%
Missing227190
Missing (%)76.5%
Infinite0
Infinite (%)0.0%
Mean7.354385809
Minimum6.531
Maximum7.81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-03-29T00:27:41.214458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum6.531
5-th percentile7.18
Q17.303
median7.36
Q37.42
95-th percentile7.5
Maximum7.81
Range1.279
Interquartile range (IQR)0.117

Descriptive statistics

Standard deviation0.1011315908
Coefficient of variation (CV)0.01375119466
Kurtosis2.928156585
Mean7.354385809
Median Absolute Deviation (MAD)0.06
Skewness-0.9227259851
Sum513880.354
Variance0.01022759866
MonotonicityNot monotonic
2022-03-29T00:27:41.337445image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.382388
 
0.8%
7.362368
 
0.8%
7.372264
 
0.8%
7.352230
 
0.8%
7.42216
 
0.7%
7.342206
 
0.7%
7.332110
 
0.7%
7.392108
 
0.7%
7.411990
 
0.7%
7.321942
 
0.7%
Other values (701)48052
 
16.2%
(Missing)227190
76.5%
ValueCountFrequency (%)
6.5312
< 0.1%
6.6112
< 0.1%
6.6462
< 0.1%
6.712
< 0.1%
6.722
< 0.1%
6.7242
< 0.1%
6.7372
< 0.1%
6.742
< 0.1%
6.7432
< 0.1%
6.7632
< 0.1%
ValueCountFrequency (%)
7.812
 
< 0.1%
7.7782
 
< 0.1%
7.7152
 
< 0.1%
7.714
< 0.1%
7.7062
 
< 0.1%
7.7052
 
< 0.1%
7.78
< 0.1%
7.692
 
< 0.1%
7.6892
 
< 0.1%
7.6842
 
< 0.1%

hematocrit
Real number (ℝ≥0)

MISSING

Distinct546
Distinct (%)0.2%
Missing63870
Missing (%)21.5%
Infinite0
Infinite (%)0.0%
Mean32.60035121
Minimum6
Maximum72.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-03-29T00:27:41.456495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile21.5
Q127.6
median32.7
Q337.4
95-th percentile43.6
Maximum72.7
Range66.7
Interquartile range (IQR)9.8

Descriptive statistics

Standard deviation6.910791791
Coefficient of variation (CV)0.2119851945
Kurtosis-0.06545260309
Mean32.60035121
Median Absolute Deviation (MAD)4.9
Skewness0.08829082339
Sum7602206.3
Variance47.75904318
MonotonicityNot monotonic
2022-03-29T00:27:41.568632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
342142
 
0.7%
332106
 
0.7%
322086
 
0.7%
352064
 
0.7%
362058
 
0.7%
312010
 
0.7%
291992
 
0.7%
301946
 
0.7%
281928
 
0.6%
371816
 
0.6%
Other values (536)213046
71.7%
(Missing)63870
 
21.5%
ValueCountFrequency (%)
62
< 0.1%
6.12
< 0.1%
6.62
< 0.1%
6.82
< 0.1%
7.12
< 0.1%
7.22
< 0.1%
7.34
< 0.1%
7.54
< 0.1%
7.72
< 0.1%
7.82
< 0.1%
ValueCountFrequency (%)
72.72
< 0.1%
672
< 0.1%
66.12
< 0.1%
65.32
< 0.1%
652
< 0.1%
64.42
< 0.1%
64.22
< 0.1%
63.62
< 0.1%
62.54
< 0.1%
62.42
< 0.1%

creatinine
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1629
Distinct (%)0.7%
Missing58328
Missing (%)19.6%
Infinite0
Infinite (%)0.0%
Mean1.556179336
Minimum0.1
Maximum24.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-03-29T00:27:41.690117image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.49
Q10.72
median1
Q31.6
95-th percentile4.75
Maximum24.95
Range24.85
Interquartile range (IQR)0.88

Descriptive statistics

Standard deviation1.73089784
Coefficient of variation (CV)1.112274016
Kurtosis25.20685214
Mean1.556179336
Median Absolute Deviation (MAD)0.35
Skewness4.186278797
Sum371516.03
Variance2.996007334
MonotonicityNot monotonic
2022-03-29T00:27:41.818591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.87760
 
2.6%
0.77508
 
2.5%
0.96038
 
2.0%
0.65828
 
2.0%
1.14066
 
1.4%
13928
 
1.3%
1.23574
 
1.2%
0.53560
 
1.2%
1.33078
 
1.0%
1.42574
 
0.9%
Other values (1619)190822
64.2%
(Missing)58328
 
19.6%
ValueCountFrequency (%)
0.126
< 0.1%
0.116
 
< 0.1%
0.1212
 
< 0.1%
0.1310
 
< 0.1%
0.1412
 
< 0.1%
0.1516
< 0.1%
0.1614
< 0.1%
0.1730
< 0.1%
0.1828
< 0.1%
0.1922
< 0.1%
ValueCountFrequency (%)
24.952
< 0.1%
24.62
< 0.1%
24.32
< 0.1%
23.92
< 0.1%
23.872
< 0.1%
23.692
< 0.1%
23.62
< 0.1%
23.432
< 0.1%
23.32
< 0.1%
23.12
< 0.1%

albumin
Real number (ℝ≥0)

MISSING

Distinct53
Distinct (%)< 0.1%
Missing179366
Missing (%)60.4%
Infinite0
Infinite (%)0.0%
Mean2.871447433
Minimum1
Maximum7.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-03-29T00:27:41.939690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.7
Q12.4
median2.9
Q33.4
95-th percentile4
Maximum7.4
Range6.4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6949734124
Coefficient of variation (CV)0.2420289518
Kurtosis-0.235055191
Mean2.871447433
Median Absolute Deviation (MAD)0.5
Skewness-0.02032236431
Sum337963.62
Variance0.482988044
MonotonicityNot monotonic
2022-03-29T00:27:42.051607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.16620
 
2.2%
2.96590
 
2.2%
2.86570
 
2.2%
36556
 
2.2%
3.26196
 
2.1%
2.75972
 
2.0%
2.65892
 
2.0%
3.35722
 
1.9%
2.55510
 
1.9%
2.45084
 
1.7%
Other values (43)56986
 
19.2%
(Missing)179366
60.4%
ValueCountFrequency (%)
1156
 
0.1%
1.1316
 
0.1%
1.2464
 
0.2%
1.3618
 
0.2%
1.4746
 
0.3%
1.51106
0.4%
1.61564
0.5%
1.71820
0.6%
1.82190
0.7%
1.92714
0.9%
ValueCountFrequency (%)
7.42
 
< 0.1%
6.62
 
< 0.1%
66
 
< 0.1%
5.74
 
< 0.1%
5.64
 
< 0.1%
5.54
 
< 0.1%
5.46
 
< 0.1%
5.310
< 0.1%
5.212
< 0.1%
5.116
< 0.1%

pao2
Real number (ℝ≥0)

MISSING

Distinct2321
Distinct (%)3.3%
Missing227190
Missing (%)76.5%
Infinite0
Infinite (%)0.0%
Mean130.2230529
Minimum9
Maximum636
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-03-29T00:27:42.170550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile53.365
Q176
median102
Q3153
95-th percentile317
Maximum636
Range627
Interquartile range (IQR)77

Descriptive statistics

Standard deviation85.09166493
Coefficient of variation (CV)0.6534301187
Kurtosis4.845093621
Mean130.2230529
Median Absolute Deviation (MAD)32
Skewness2.063839751
Sum9099205.6
Variance7240.59144
MonotonicityNot monotonic
2022-03-29T00:27:42.282649image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75666
 
0.2%
76662
 
0.2%
82656
 
0.2%
70648
 
0.2%
78644
 
0.2%
77644
 
0.2%
80640
 
0.2%
79636
 
0.2%
69626
 
0.2%
71626
 
0.2%
Other values (2311)63426
 
21.4%
(Missing)227190
76.5%
ValueCountFrequency (%)
92
 
< 0.1%
152
 
< 0.1%
162
 
< 0.1%
176
< 0.1%
184
< 0.1%
18.52
 
< 0.1%
18.92
 
< 0.1%
196
< 0.1%
19.72
 
< 0.1%
204
< 0.1%
ValueCountFrequency (%)
6362
< 0.1%
6202
< 0.1%
6072
< 0.1%
6022
< 0.1%
601.62
< 0.1%
5992
< 0.1%
5972
< 0.1%
5872
< 0.1%
5822
< 0.1%
5792
< 0.1%

pco2
Real number (ℝ≥0)

MISSING

Distinct894
Distinct (%)1.3%
Missing227190
Missing (%)76.5%
Infinite0
Infinite (%)0.0%
Mean42.92129261
Minimum6.9
Maximum147.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-03-29T00:27:42.413937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum6.9
5-th percentile26.4
Q134.8
median40.8
Q348
95-th percentile69.1
Maximum147.3
Range140.4
Interquartile range (IQR)13.2

Descriptive statistics

Standard deviation13.38807964
Coefficient of variation (CV)0.311921632
Kurtosis4.844517185
Mean42.92129261
Median Absolute Deviation (MAD)6.6
Skewness1.620548408
Sum2999082.4
Variance179.2406764
MonotonicityNot monotonic
2022-03-29T00:27:42.539840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
411864
 
0.6%
381862
 
0.6%
401738
 
0.6%
371712
 
0.6%
421692
 
0.6%
391666
 
0.6%
431614
 
0.5%
351582
 
0.5%
361558
 
0.5%
441408
 
0.5%
Other values (884)53178
 
17.9%
(Missing)227190
76.5%
ValueCountFrequency (%)
6.92
 
< 0.1%
72
 
< 0.1%
7.92
 
< 0.1%
82
 
< 0.1%
8.42
 
< 0.1%
92
 
< 0.1%
9.52
 
< 0.1%
9.72
 
< 0.1%
1012
< 0.1%
10.22
 
< 0.1%
ValueCountFrequency (%)
147.32
< 0.1%
147.12
< 0.1%
145.82
< 0.1%
143.12
< 0.1%
1412
< 0.1%
139.82
< 0.1%
1394
< 0.1%
136.42
< 0.1%
1362
< 0.1%
1302
< 0.1%

bun
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct624
Distinct (%)0.3%
Missing59454
Missing (%)20.0%
Infinite0
Infinite (%)0.0%
Mean26.81385809
Minimum1
Maximum254
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-03-29T00:27:42.713082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q113
median19.6
Q333
95-th percentile71
Maximum254
Range253
Interquartile range (IQR)20

Descriptive statistics

Standard deviation22.08119802
Coefficient of variation (CV)0.8234994737
Kurtosis9.044717306
Mean26.81385809
Median Absolute Deviation (MAD)8.6
Skewness2.448995373
Sum6371240.82
Variance487.5793061
MonotonicityNot monotonic
2022-03-29T00:27:42.842436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1310176
 
3.4%
149920
 
3.3%
159842
 
3.3%
129668
 
3.3%
119540
 
3.2%
169068
 
3.1%
108746
 
2.9%
178430
 
2.8%
187828
 
2.6%
97624
 
2.6%
Other values (614)146768
49.4%
(Missing)59454
20.0%
ValueCountFrequency (%)
138
 
< 0.1%
2228
 
0.1%
2.82
 
< 0.1%
2.92
 
< 0.1%
3686
0.2%
3.32
 
< 0.1%
3.42
 
< 0.1%
3.56
 
< 0.1%
3.62
 
< 0.1%
3.72
 
< 0.1%
ValueCountFrequency (%)
2544
< 0.1%
2532
 
< 0.1%
2522
 
< 0.1%
2492
 
< 0.1%
2484
< 0.1%
2442
 
< 0.1%
2386
< 0.1%
2372
 
< 0.1%
2352
 
< 0.1%
2332
 
< 0.1%

glucose
Real number (ℝ≥0)

MISSING

Distinct1000
Distinct (%)0.4%
Missing33346
Missing (%)11.2%
Infinite0
Infinite (%)0.0%
Mean163.5492943
Minimum1
Maximum2357
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-03-29T00:27:42.963415image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile72
Q196
median135
Q3199
95-th percentile348
Maximum2357
Range2356
Interquartile range (IQR)103

Descriptive statistics

Standard deviation101.6885031
Coefficient of variation (CV)0.6217605742
Kurtosis18.90355627
Mean163.5492943
Median Absolute Deviation (MAD)45
Skewness2.973166091
Sum43130892.8
Variance10340.55167
MonotonicityNot monotonic
2022-03-29T00:27:43.086865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
923002
 
1.0%
962972
 
1.0%
952926
 
1.0%
992924
 
1.0%
972914
 
1.0%
942892
 
1.0%
932862
 
1.0%
982850
 
1.0%
912830
 
1.0%
1002784
 
0.9%
Other values (990)234762
79.0%
(Missing)33346
 
11.2%
ValueCountFrequency (%)
14
 
< 0.1%
38
 
< 0.1%
42
 
< 0.1%
52
 
< 0.1%
610
 
< 0.1%
84
 
< 0.1%
910
 
< 0.1%
1016
< 0.1%
1118
< 0.1%
1228
< 0.1%
ValueCountFrequency (%)
23572
< 0.1%
17312
< 0.1%
16912
< 0.1%
16882
< 0.1%
16442
< 0.1%
15102
< 0.1%
14952
< 0.1%
14782
< 0.1%
14642
< 0.1%
14612
< 0.1%

bilirubin
Real number (ℝ≥0)

MISSING

Distinct628
Distinct (%)0.6%
Missing190228
Missing (%)64.0%
Infinite0
Infinite (%)0.0%
Mean1.222612602
Minimum0.1
Maximum60.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-03-29T00:27:43.207048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.2
Q10.4
median0.7
Q31.1
95-th percentile3.6
Maximum60.2
Range60.1
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation2.441958125
Coefficient of variation (CV)1.997327788
Kurtosis103.358318
Mean1.222612602
Median Absolute Deviation (MAD)0.3
Skewness8.605415897
Sum130619.04
Variance5.963159485
MonotonicityNot monotonic
2022-03-29T00:27:43.319010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.412740
 
4.3%
0.511992
 
4.0%
0.310722
 
3.6%
0.610014
 
3.4%
0.78330
 
2.8%
0.86846
 
2.3%
0.25358
 
1.8%
0.95236
 
1.8%
14376
 
1.5%
1.13288
 
1.1%
Other values (618)27934
 
9.4%
(Missing)190228
64.0%
ValueCountFrequency (%)
0.1664
 
0.2%
0.122
 
< 0.1%
0.132
 
< 0.1%
0.148
 
< 0.1%
0.158
 
< 0.1%
0.162
 
< 0.1%
0.1710
 
< 0.1%
0.186
 
< 0.1%
0.194
 
< 0.1%
0.25358
1.8%
ValueCountFrequency (%)
60.22
< 0.1%
522
< 0.1%
51.22
< 0.1%
512
< 0.1%
50.92
< 0.1%
482
< 0.1%
46.42
< 0.1%
45.62
< 0.1%
44.82
< 0.1%
44.52
< 0.1%

fio2
Real number (ℝ≥0)

MISSING

Distinct89
Distinct (%)0.1%
Missing227190
Missing (%)76.5%
Infinite0
Infinite (%)0.0%
Mean59.27130549
Minimum21
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-03-29T00:27:43.437853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile25
Q140
median50
Q380
95-th percentile100
Maximum100
Range79
Interquartile range (IQR)40

Descriptive statistics

Standard deviation26.28954427
Coefficient of variation (CV)0.4435458954
Kurtosis-1.149469014
Mean59.27130549
Median Absolute Deviation (MAD)15
Skewness0.4990085749
Sum4141523.2
Variance691.1401378
MonotonicityNot monotonic
2022-03-29T00:27:43.557352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10016090
 
5.4%
4012714
 
4.3%
5012106
 
4.1%
606882
 
2.3%
303912
 
1.3%
212866
 
1.0%
802506
 
0.8%
702458
 
0.8%
351900
 
0.6%
281420
 
0.5%
Other values (79)7020
 
2.4%
(Missing)227190
76.5%
ValueCountFrequency (%)
212866
1.0%
2216
 
< 0.1%
2360
 
< 0.1%
24130
 
< 0.1%
25908
 
0.3%
2696
 
< 0.1%
27306
 
0.1%
281420
 
0.5%
2954
 
< 0.1%
303912
1.3%
ValueCountFrequency (%)
10016090
5.4%
99.62
 
< 0.1%
9924
 
< 0.1%
98.82
 
< 0.1%
98.54
 
< 0.1%
98.42
 
< 0.1%
98.22
 
< 0.1%
9826
 
< 0.1%
97.32
 
< 0.1%
9716
 
< 0.1%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing106
Missing (%)< 0.1%
Memory size290.3 KiB
0.0
160834 
1.0
136124 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0160834
54.1%
1.0136124
45.8%
(Missing)106
 
< 0.1%

Length

2022-03-29T00:27:43.670440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-29T00:27:43.726059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0160834
54.2%
1.0136124
45.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

age
Real number (ℝ≥0)

ZEROS

Distinct86
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.87528613
Minimum0
Maximum89
Zeros10446
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2022-03-29T00:27:43.800699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21
Q151
median63
Q374
95-th percentile85
Maximum89
Range89
Interquartile range (IQR)23

Descriptive statistics

Standard deviation19.92545426
Coefficient of variation (CV)0.3327826145
Kurtosis1.078101658
Mean59.87528613
Median Absolute Deviation (MAD)12
Skewness-1.073214263
Sum17786792
Variance397.0237276
MonotonicityNot monotonic
2022-03-29T00:27:43.918590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
010446
 
3.5%
677490
 
2.5%
687124
 
2.4%
717008
 
2.4%
726992
 
2.4%
656854
 
2.3%
666824
 
2.3%
706696
 
2.3%
636586
 
2.2%
626514
 
2.2%
Other values (76)224530
75.6%
ValueCountFrequency (%)
010446
3.5%
14
 
< 0.1%
44
 
< 0.1%
74
 
< 0.1%
84
 
< 0.1%
96
 
< 0.1%
104
 
< 0.1%
1110
 
< 0.1%
1214
 
< 0.1%
1322
 
< 0.1%
ValueCountFrequency (%)
892862
1.0%
883284
1.1%
873774
1.3%
864012
1.4%
854426
1.5%
844832
1.6%
835030
1.7%
825020
1.7%
815168
1.7%
805194
1.7%

admitdiagnosis
Categorical

HIGH CARDINALITY

Distinct425
Distinct (%)0.1%
Missing1892
Missing (%)0.6%
Memory size599.8 KiB
SEPSISPULM
 
14704
AMI
 
12276
CVASTROKE
 
11128
CHF
 
10636
SEPSISUTI
 
8908
Other values (420)
237520 

Length

Max length10
Median length9
Mean length8.11530904
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRHYTHATR
2nd rowRHYTHATR
3rd rowSEPSISUTI
4th rowSEPSISUTI
5th rowRESPARREST

Common Values

ValueCountFrequency (%)
SEPSISPULM14704
 
4.9%
AMI12276
 
4.1%
CVASTROKE11128
 
3.7%
CHF10636
 
3.6%
SEPSISUTI8908
 
3.0%
DKA8298
 
2.8%
S-CABG8086
 
2.7%
RHYTHATR7870
 
2.6%
CARDARREST7460
 
2.5%
EMPHYSBRON7320
 
2.5%
Other values (415)198486
66.8%

Length

2022-03-29T00:27:44.040392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sepsispulm14704
 
5.0%
ami12276
 
4.2%
cvastroke11128
 
3.8%
chf10636
 
3.6%
sepsisuti8908
 
3.0%
dka8298
 
2.8%
s-cabg8086
 
2.7%
rhythatr7870
 
2.7%
cardarrest7460
 
2.5%
emphysbron7320
 
2.5%
Other values (415)198486
67.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

aids
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size290.3 KiB
0
296740 
1
 
324

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0296740
99.9%
1324
 
0.1%

Length

2022-03-29T00:27:44.319576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-29T00:27:44.374706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0296740
99.9%
1324
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

hepaticfailure
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size290.3 KiB
0
292374 
1
 
4690

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0292374
98.4%
14690
 
1.6%

Length

2022-03-29T00:27:44.427942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-29T00:27:44.486485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0292374
98.4%
14690
 
1.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

lymphoma
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size290.3 KiB
0
295736 
1
 
1328

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0295736
99.6%
11328
 
0.4%

Length

2022-03-29T00:27:44.540400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-29T00:27:44.599671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0295736
99.6%
11328
 
0.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

metastaticcancer
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size290.3 KiB
0
290858 
1
 
6206

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0290858
97.9%
16206
 
2.1%

Length

2022-03-29T00:27:44.652955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-29T00:27:44.709440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0290858
97.9%
16206
 
2.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

leukemia
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size290.3 KiB
0
294820 
1
 
2244

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0294820
99.2%
12244
 
0.8%

Length

2022-03-29T00:27:44.762430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-29T00:27:44.819295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0294820
99.2%
12244
 
0.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size290.3 KiB
0
288884 
1
 
8180

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0288884
97.2%
18180
 
2.8%

Length

2022-03-29T00:27:44.872598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-29T00:27:44.931848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0288884
97.2%
18180
 
2.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

cirrhosis
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size290.3 KiB
0
291662 
1
 
5402

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0291662
98.2%
15402
 
1.8%

Length

2022-03-29T00:27:44.987552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-29T00:27:45.043254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0291662
98.2%
15402
 
1.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

electivesurgery
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size290.3 KiB
0
244720 
1
52344 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0244720
82.4%
152344
 
17.6%

Length

2022-03-29T00:27:45.101154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-29T00:27:45.157293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0244720
82.4%
152344
 
17.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

activetx
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size290.3 KiB
1
172498 
0
124566 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1172498
58.1%
0124566
41.9%

Length

2022-03-29T00:27:45.211160image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-29T00:27:45.269076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1172498
58.1%
0124566
41.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

readmit
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size290.3 KiB
0
280524 
1
 
16540

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0280524
94.4%
116540
 
5.6%

Length

2022-03-29T00:27:45.324474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-29T00:27:45.381523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0280524
94.4%
116540
 
5.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

diabetes
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size290.3 KiB
0
227698 
1
69366 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0227698
76.6%
169366
 
23.4%

Length

2022-03-29T00:27:45.437595image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-29T00:27:45.494156image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0227698
76.6%
169366
 
23.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size290.3 KiB
ALIVE
279834 
EXPIRED
 
17230

Length

Max length7
Median length5
Mean length5.116001939
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEXPIRED
2nd rowEXPIRED
3rd rowALIVE
4th rowALIVE
5th rowALIVE

Common Values

ValueCountFrequency (%)
ALIVE279834
94.2%
EXPIRED17230
 
5.8%

Length

2022-03-29T00:27:45.555290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-29T00:27:45.621094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
alive279834
94.2%
expired17230
 
5.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size290.3 KiB
ALIVE
269590 
EXPIRED
27474 

Length

Max length7
Median length5
Mean length5.184970242
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEXPIRED
2nd rowEXPIRED
3rd rowALIVE
4th rowALIVE
5th rowALIVE

Common Values

ValueCountFrequency (%)
ALIVE269590
90.8%
EXPIRED27474
 
9.2%

Length

2022-03-29T00:27:45.681923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-29T00:27:45.748153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
alive269590
90.8%
expired27474
 
9.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-03-29T00:27:29.606276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:22.290780image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:26.041538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:29.421867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:33.168543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:36.717218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:39.780041image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:42.999084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:46.313887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:49.680139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:53.053393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:56.591301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:00.034099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:02.716392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:06.012159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:09.052972image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:11.946965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:14.558744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:17.369628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:20.566085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:23.966450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:26.703560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:29.793427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:22.485712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:26.267279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:29.603399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:33.404312image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:36.873667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:39.938941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:43.160969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:46.480320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:49.844491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:53.235922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:56.764885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:00.154242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:02.872522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:06.161392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:09.180880image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:12.073161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:14.677737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:17.533776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:20.731585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:24.090439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:26.828089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:29.962475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:22.653589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:26.440124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:29.782341image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:33.605112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:37.006881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:40.091692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:43.320494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:46.640477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:49.997054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:53.418567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:56.928389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:00.270017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:03.021200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:06.307344image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:09.307169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:12.191905image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:14.793972image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:17.684206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:20.885620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:24.216149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:26.954017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:30.153857image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:22.831404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:26.617097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:29.968031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:33.806804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:37.150015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:40.254053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:43.492678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:46.817990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:50.158728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:53.613374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:57.105369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:00.395904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:03.185027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:06.468220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:09.446000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:12.320928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:14.920106image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:17.844363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:21.079525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:24.354922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:27.091574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:30.283367image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:22.970867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:26.753975image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:30.115359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:33.951548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:37.277248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:40.379441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:43.618656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:46.944775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:50.286955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:53.767501image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:57.235933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:00.506709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:03.310385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:06.584739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:09.556911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:12.440964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:15.031758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:17.968373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:21.207857image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:24.472394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:27.206947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:30.427821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:23.119953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:26.897698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:30.263403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:34.101447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:37.408322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:40.523621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:43.765528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:47.085684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:50.436022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:53.931730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:57.386828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:00.623507image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:03.452620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:06.724752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:09.680155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:12.562884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:15.154888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:18.110114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:21.350523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:24.595083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:27.517462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:30.595605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:23.290825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:27.085668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:30.433746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:34.272752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:37.553141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:40.674535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:43.919295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:47.246293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:50.591376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:54.116798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:57.562621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:00.745745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:03.599111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:06.871432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:09.807107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:12.687010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:15.459069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:18.257956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:21.723122image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:24.720672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:27.638615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:30.753550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:23.444970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:27.241736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:30.597561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:34.437133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:37.681925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:40.815983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:44.067706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:47.403366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:50.740896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:54.282099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:57.717463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:00.860252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:03.931829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:07.013008image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:10.109738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:12.803079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:15.570433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:18.397410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:21.865323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:24.841407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:27.751862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:30.897769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:23.599191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:27.395142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:30.749009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:34.596933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:37.813680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:40.961170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:44.207727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:47.555858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:50.884830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:54.441484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-03-29T00:27:08.391806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:11.318417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:13.973569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:16.741735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:19.771153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:23.277165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:26.077016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:28.946298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:32.345022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:25.441053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:28.829702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:32.502600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:36.133511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:39.263828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:42.414777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:45.756426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:49.051558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:52.474604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:55.980136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:59.477430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:02.185310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:05.464513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:08.535681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:11.436939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:14.089065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:16.857285image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:19.913994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:23.422795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:26.203314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:29.063596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:32.475166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:25.566090image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:28.949424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:32.635512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:36.254497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:39.371614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:42.543863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:45.869477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:49.172531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:52.593251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:56.109146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:59.599696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:02.301917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:05.580792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:08.651351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:11.573489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:14.201965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:16.971005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:20.039554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:23.542303image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:26.332244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:29.179667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:32.592362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:25.684953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:29.069092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:32.779803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:36.371656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:39.487303image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:42.660417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:45.982504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:49.333017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:52.707413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:56.232246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:59.732913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:02.428284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:05.697742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:08.763508image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:11.696939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:14.317526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:17.085625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:20.171842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:23.659235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:26.446167image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:29.296079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:32.769440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:25.870563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:29.253837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:32.981899image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:36.586395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:39.633151image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:42.831363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:46.153100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:49.527859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:52.878523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:56.423345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:26:59.916401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:02.560765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:05.862191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:08.928052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:11.832636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:14.447198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:17.210173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:20.376203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:23.838305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:26.586916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T00:27:29.426417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-03-29T00:27:45.826689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Missing values

2022-03-29T00:27:33.171762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-03-29T00:27:34.579870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-03-29T00:27:37.282280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-03-29T00:27:38.116925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

intubatedventdialysiseyesmotorverbalmedsurinewbctemperaturerespiratoryratesodiumheartratemeanbpphhematocritcreatininealbuminpao2pco2bunglucosebilirubinfio2genderageadmitdiagnosisaidshepaticfailurelymphomametastaticcancerleukemiaimmunosuppressioncirrhosiselectivesurgeryactivetxreadmitdiabetesactualicumortalityactualhospitalmortality
00004650NaN14.736.130.0139.014062.0NaN40.12.303.1NaNNaN27.095.04.1NaN1.070RHYTHATR00000000100EXPIREDEXPIRED
10004650NaN14.736.130.0139.014062.0NaN40.12.303.1NaNNaN27.095.04.1NaN1.070RHYTHATR00000000100EXPIREDEXPIRED
20003640NaN14.139.336.0134.011840.0NaN27.42.512.3NaNNaN31.0168.00.4NaN0.068SEPSISUTI00000000001ALIVEALIVE
30003640NaN14.139.336.0134.011840.0NaN27.42.512.3NaNNaN31.0168.00.4NaN0.068SEPSISUTI00000000001ALIVEALIVE
40101310NaN12.735.133.0145.012046.07.4536.90.56NaN51.037.09.0145.0NaN100.01.077RESPARREST00000000101ALIVEALIVE
50101310NaN12.735.133.0145.012046.07.4536.90.56NaN51.037.09.0145.0NaN100.01.077RESPARREST00000000101ALIVEALIVE
60003650NaNNaN36.737.0NaN10268.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.025ODSEDHYP00000000000ALIVEALIVE
70003650NaNNaN36.737.0NaN10268.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.025ODSEDHYP00000000000ALIVEALIVE
80103640NaN42.740.154.0133.0204198.07.4626.21.90NaN65.023.032.0145.0NaN21.00.082SEPSISPULM00000000100ALIVEALIVE
90103640NaN42.740.154.0133.0204198.07.4626.21.90NaN65.023.032.0145.0NaN21.00.082SEPSISPULM00000000100ALIVEALIVE

Last rows

intubatedventdialysiseyesmotorverbalmedsurinewbctemperaturerespiratoryratesodiumheartratemeanbpphhematocritcreatininealbuminpao2pco2bunglucosebilirubinfio2genderageadmitdiagnosisaidshepaticfailurelymphomametastaticcancerleukemiaimmunosuppressioncirrhosiselectivesurgeryactivetxreadmitdiabetesactualicumortalityactualhospitalmortality
29705400046501751.1552NaN36.250.0NaN106120.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.050CHF00000000000ALIVEALIVE
29705500046501751.1552NaN36.250.0NaN106120.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.050CHF00000000000ALIVEALIVE
29705600046405724.77769.536.442.0140.0112125.0NaN39.01.073.5NaNNaN32.0139.00.7NaN1.079PULMEMBOL00000000000ALIVEALIVE
29705700046405724.77769.536.442.0140.0112125.0NaN39.01.073.5NaNNaN32.0139.00.7NaN1.079PULMEMBOL00000000000ALIVEALIVE
2970581101110358.9920NaN32.934.0142.010458.07.224NaN2.43NaN80.044.030.0346.0NaN100.00.073CARDARREST00000000101ALIVEALIVE
2970591101110358.9920NaN32.934.0142.010458.07.224NaN2.43NaN80.044.030.0346.0NaN100.00.073CARDARREST00000000101ALIVEALIVE
29706000046502171.6640NaN35.531.0NaN8360.0NaN32.0NaNNaNNaNNaNNaN137.0NaNNaN0.081LOWGIBLEED00000000100ALIVEALIVE
29706100046502171.6640NaN35.531.0NaN8360.0NaN32.0NaNNaNNaNNaNNaN137.0NaNNaN0.081LOWGIBLEED00000000100ALIVEALIVE
2970620004650NaNNaN37.157.0NaN97118.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.035PULMEMBOL00000000000ALIVEALIVE
2970630004650NaNNaN37.157.0NaN97118.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.035PULMEMBOL00000000000ALIVEALIVE